code: def create_rename_keys_dpt(config):
      rename_keys = []
  
      # fmt: off
      # activation postprocessing (projections, readout projections + resize blocks)
      for i in range(4):
          rename_keys.append((f"decode_head.reassemble_blocks.projects.{i}.conv.weight", f"neck.reassemble_stage.layers.{i}.projection.weight"))
          rename_keys.append((f"decode_head.reassemble_blocks.projects.{i}.conv.bias", f"neck.reassemble_stage.layers.{i}.projection.bias"))
  
          rename_keys.append((f"decode_head.reassemble_blocks.readout_projects.{i}.0.weight", f"neck.reassemble_stage.readout_projects.{i}.0.weight"))
          rename_keys.append((f"decode_head.reassemble_blocks.readout_projects.{i}.0.bias", f"neck.reassemble_stage.readout_projects.{i}.0.bias"))
  
          if i != 2:
              rename_keys.append((f"decode_head.reassemble_blocks.resize_layers.{i}.weight", f"neck.reassemble_stage.layers.{i}.resize.weight"))
              rename_keys.append((f"decode_head.reassemble_blocks.resize_layers.{i}.bias", f"neck.reassemble_stage.layers.{i}.resize.bias"))
  
      # fusion layers
      for i in range(4):
          rename_keys.append((f"decode_head.fusion_blocks.{i}.project.conv.weight", f"neck.fusion_stage.layers.{i}.projection.weight"))
          rename_keys.append((f"decode_head.fusion_blocks.{i}.project.conv.bias", f"neck.fusion_stage.layers.{i}.projection.bias"))
          if i != 0:
              rename_keys.append((f"decode_head.fusion_blocks.{i}.res_conv_unit1.conv1.conv.weight", f"neck.fusion_stage.layers.{i}.residual_layer1.convolution1.weight"))
              rename_keys.append((f"decode_head.fusion_blocks.{i}.res_conv_unit1.conv2.conv.weight", f"neck.fusion_stage.layers.{i}.residual_layer1.convolution2.weight"))
          rename_keys.append((f"decode_head.fusion_blocks.{i}.res_conv_unit2.conv1.conv.weight", f"neck.fusion_stage.layers.{i}.residual_layer2.convolution1.weight"))
          rename_keys.append((f"decode_head.fusion_blocks.{i}.res_conv_unit2.conv2.conv.weight", f"neck.fusion_stage.layers.{i}.residual_layer2.convolution2.weight"))
  
      # neck convolutions
      for i in range(4):
          rename_keys.append((f"decode_head.convs.{i}.conv.weight", f"neck.convs.{i}.weight"))
  
      # head
      rename_keys.append(("decode_head.project.conv.weight", "head.projection.weight"))
      rename_keys.append(("decode_head.project.conv.bias", "head.projection.bias"))
  
      for i in range(0, 5, 2):
          rename_keys.append((f"decode_head.conv_depth.head.{i}.weight", f"head.head.{i}.weight"))
          rename_keys.append((f"decode_head.conv_depth.head.{i}.bias", f"head.head.{i}.bias"))
      # fmt: on
  
      return rename_keys
  
file_path: transformers/src/transformers/models/dpt/convert_dinov2_depth_to_hf.py
imports: import argparse
  import itertools
  import math
  from pathlib import Path
  import requests
  import torch
  from PIL import Image
  from torchvision import transforms
  from transformers import Dinov2Config, DPTConfig, DPTForDepthEstimation, DPTImageProcessor
  from transformers.utils import logging
label_desc: Use image node_id 2 for calling a global function with example usage: create_rename_keys_dpt(config) and returns: rename_keys